Predicting Diabetes

Inhoudsopgave - Leander

• De data
• Onze research vraag
• Modellen
• Het beste model
• Assumpties
• Conclusie model
• Antwoord research vraag

De Dataset

National Institute of Diabetes and Digestive and Kidney Diseases (1990)

Data uitleggen - Leander

• Pregnancies: Number of times pregnant

• Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test

• BloodPressure: Diastolic blood pressure (mm Hg)

• SkinThickness: Triceps skin fold thickness (mm)

• Insulin: 2-Hour serum insulin (mu U/ml)

• BMI: Body mass index (weight in kg/(height in m)^2)

• DiabetesPedigreeFunction: Diabetes pedigree function

• Age: Age (years)

• Outcome: Class variable (0 or 1)

Inlezen data Leander

datacsv <- read.csv("./data/raw/diabetes.csv", header = TRUE)
saveRDS(datacsv, "./data/raw/dataset.rds")

# Preprocessing
dataRDS <- readRDS("./data/raw/dataset.rds")
dataRDS <- na.omit(dataRDS) # removes NA from important columns
saveRDS(dataRDS, "./data/processed/dataset.rds")
dataset <- readRDS("./data/processed/dataset.rds")

# Recoding BMI to classes, WAAROM OOKALWEER??
dataRDS$BMI[dataRDS$BMI <= 18.5 ] <- 1                     # Underweight
dataRDS$BMI[dataRDS$BMI > 18.5 & dataRDS$BMI <= 25  ] <- 2 # Healthy
dataRDS$BMI[dataRDS$BMI > 25 & dataRDS$BMI <= 30 ] <- 3    # Overweight
dataRDS$BMI[dataRDS$BMI > 30] <- 4                         # Obese

# Removing missing values
dataRDS <- subset(dataRDS,
                     BloodPressure != 0 &
                     SkinThickness != 0 &
                     Glucose != 0 &
                     Insulin != 0 &
                     BMI != 0)

Interessante bevindingen - Leander

Research Questions - Leander

Can we predict if someone is diabetic using the previously stated variables with a logistic regression?

Beste model kiezen en uitleggen (leg uit wat het beste model is, wat het doet en waarom het het beste is) - August

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assumptions, welke assumptions, waarom ze voldoen - Friso

Binary dependent variable

• Diabetes (coded 1)
• Not diabetes (coded 0)

Sufficiently large sample size

• 10 cases per candidate predictor : \(N=\frac{10k}{p}\)

  Outcome   n      prop
1       0 263 0.6692112
2       1 130 0.3307888

[1] 61

Full-rank predictor matrix

• More observations than predictors

• No multicollinearity among linear predictors

                     Age                  Glucose                      BMI 
                1.022157                 1.018869                 1.009314 
DiabetesPedigreeFunction 
                1.006276 

assumptions, delete outliers, laat zien dat ie niet van belang is op case by case basis - Friso

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Exploring the model section, ga in op het model met onderzoeksmethode uit slides INCLUDING CONFUSION MATRIX - Demi

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Concludeer en beantwoord - Demi

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